Feasibility of artificial intelligence-driven interfractional monitoring of organ changes by mega-voltage computed tomography in intensity-modulated radiotherapy of prostate cancer

• 영문명: Feasibility of artificial intelligence-driven interfractional monitoring of organ changes by mega-voltage computed tomography in intensity-modulated radiotherapy of prostate cancer
• 발행기관: 대한방사선종양학회
• 저자명: Yohan Lee Hyun Joon Choi Hyemi Kim Sunghyun Kim Mi Sun Kim Hyejung Cha Young Ju Eum Hyosung Cho
• 간행물 정보: 『대한방사선종양학회지』제41권 제3호, 186~198쪽, 전체 13쪽
• 주제분류: 의약학 > 종양학
• 파일형태: PDF
• 발행일자: 2023.09.30

영문 초록

Purpose: High-dose radiotherapy (RT) for localized prostate cancer requires careful consideration of target position changes and adjacent organs-at-risk (OARs), such as the rectum and bladder. Therefore, daily monitoring of target position and OAR changes is crucial in minimizing interfractional dosimetric uncertainties. For efficient monitoring of the internal condition of patients, we assessed the feasibility of an auto-segmentation of OARs on the daily acquired images, such as megavoltage computed tomography (MVCT), via a commercial artificial intelligence (AI)-based solution in this study. Materials and Methods: We collected MVCT images weekly during the entire course of RT for 100 prostate cancer patients treated with the helical TomoTherapy system. Based on the manually contoured body outline, the bladder including prostate area, and rectal balloon regions for the 100 MVCT images, we trained the commercially available fully convolutional (FC)-DenseNet model and tested its auto-contouring performance. Results: Based on the optimally determined hyperparameters, the FC-DenseNet model successfully auto-contoured all regions of interest showing high dice similarity coefficient (DSC) over 0.8 and a small mean surface distance (MSD) within 1.43 mm in reference to the manually contoured data. With this well-trained AI model, we have efficiently monitored the patient’s internal condition through six MVCT scans, analyzing DSC, MSD, centroid, and volume differences. Conclusion: We have verified the feasibility of utilizing a commercial AI-based model for auto-segmentation with low-quality daily MVCT images. In the future, we will establish a fast and accurate auto-segmentation and internal organ monitoring system for efficiently determining the time for adaptive replanning.

목차

Introduction

Materials and Methods

Results

Discussion and Conclusion

Statement of Ethics

Conflict of Interest

Funding

Author Contributions

Data Availability Statement

References

키워드

Artificial intelligence Image-guided radiotherapy Intensity-modulated radiotherapy Prostate cancer X-ray tomography Patient monitoring